Data

food_consumption <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-18/food_consumption.csv')
food_consumption[1:15,] %>%
  kable()
country food_category consumption co2_emmission
Argentina Pork 10.51 37.20
Argentina Poultry 38.66 41.53
Argentina Beef 55.48 1712.00
Argentina Lamb & Goat 1.56 54.63
Argentina Fish 4.36 6.96
Argentina Eggs 11.39 10.46
Argentina Milk - inc. cheese 195.08 277.87
Argentina Wheat and Wheat Products 103.11 19.66
Argentina Rice 8.77 11.22
Argentina Soybeans 0.00 0.00
Argentina Nuts inc. Peanut Butter 0.49 0.87
Australia Pork 24.14 85.44
Australia Poultry 46.12 49.54
Australia Beef 33.86 1044.85
Australia Lamb & Goat 9.87 345.65 
food_consumption %>%
  ggplot(aes(consumption, co2_emmission)) +
  geom_point()

food_consumption %>%
  ggplot(aes(consumption, co2_emmission, colour = country)) +
  geom_point()

food_consumption %>%
  ggplot(aes(consumption, co2_emmission, colour = country)) +
  geom_point() +
  theme(legend.position = "none")

food_consumption %>%
  ggplot(aes(consumption, co2_emmission, colour = food_category)) +
  geom_point()

food_consumption %>%
  ggplot(aes(consumption, co2_emmission, colour = food_category)) +
  geom_point() +
  labs(title = "Co2 emmission vs Consumption",
       y = "kg CO2/person/year", x = "kg/person/year")

food_consumption %>%
  ggplot(aes(consumption, co2_emmission, colour = food_category)) +
  geom_point() +
  labs(title = expression('CO'[2]*" Emmission vs Consumption (per person per year)"),
       y = expression('kg CO'[2]*"/person/year"), x = "kg/person/year",
       colour = "Food Type")

From Website

‘The study analyses data from the Food and Agriculture Organization of the United Nations (FAO) to determine the quantity of produce supplied for consumption of 11 food types for all countries researched. Using CO2 emissions data, the carbon footprint per capita is then calculated for each food type.’

Quantity was recoreded, and carbon footprint calculated using this.

Look at Consumption Only

food_consumption %>%
  ggplot(aes(x = food_category, y = consumption, colour = country)) +
  geom_point() 

# Remove legend
food_consumption %>%
  ggplot(aes(x = food_category, y = consumption, colour = country)) +
  geom_point() +
  theme(legend.position = "none")

# Flip plot
food_consumption %>%
  ggplot(aes(x = food_category, y = consumption, colour = country)) +
  geom_point() +
  theme(legend.position = "none") +
  coord_flip()

# alternative
food_consumption %>%
  ggplot(aes(y = food_category, x = consumption, colour = country)) +
  geom_point() +
  theme(legend.position = "none")

#jitter
food_consumption %>%
  ggplot(aes(y = food_category, x = consumption, colour = country)) +
  geom_jitter() +
  theme(legend.position = "none")

food_consumption %>%
  ggplot(aes(y = country, x = consumption, colour = food_category)) +
  geom_jitter()

Summary Stats - Consumption by country

food_consumption %>%
  group_by(country) %>%
  summarise(average_consumption = mean(consumption)) %>%
  arrange(desc(average_consumption)) %>%
  head(15) %>% kable()
country average_consumption
Finland 58.16273
Lithuania 50.45545
Sweden 50.00000
Netherlands 48.56091
Albania 48.43000
Ireland 47.15000
Switzerland 46.80909
Italy 46.72545
Denmark 45.37000
Luxembourg 45.26364
Greece 44.87545
USA 44.65000
Norway 44.31727
France 43.56091
Maldives 43.30273 
food_consumption %>%
  group_by(country) %>%
  summarise(average_consumption = mean(consumption)) %>%
  arrange(average_consumption) %>%
  head(15) %>% kable()
country average_consumption
Rwanda 3.670909
Malawi 4.636364
Zambia 5.191818
Mozambique 5.764545
Togo 6.756364
Uganda 7.325455
Ethiopia 7.797273
Nigeria 8.566364
Zimbabwe 8.778182
Cameroon 8.826364
Tanzania 8.984545
Niger 9.194546
Ghana 9.200909
Angola 10.544545
Congo 10.666364

Ireland

food_consumption %>%
  filter(country == "Ireland") %>%
  arrange(desc(consumption)) %>%
  kable()
country food_category consumption co2_emmission
Ireland Milk - inc. cheese 291.86 415.73
Ireland Wheat and Wheat Products 107.98 20.59
Ireland Pork 32.40 114.68
Ireland Poultry 26.26 28.21
Ireland Beef 22.35 689.67
Ireland Fish 17.39 27.77
Ireland Eggs 8.96 8.23
Ireland Lamb & Goat 4.10 143.58
Ireland Nuts inc. Peanut Butter 4.10 7.26
Ireland Rice 3.00 3.84
Ireland Soybeans 0.25 0.11

By Food Type

food_consumption %>%
  filter(food_category == "Pork") %>%
  arrange(desc(consumption)) %>%
  head(10) %>% kable()
country food_category consumption co2_emmission
Hong Kong SAR. China Pork 67.11 237.54
Austria Pork 52.56 186.04
Germany Pork 51.81 183.38
Spain Pork 48.92 173.15
Poland Pork 46.19 163.49
Lithuania Pork 45.67 161.65
Luxembourg Pork 43.58 154.25
Croatia Pork 42.79 151.46
Czech Republic Pork 41.17 145.72
Belarus Pork 40.37 142.89 
food_consumption %>%
  filter(food_category == "Pork") %>%
  arrange(consumption) %>%
  head(10) %>% kable()
country food_category consumption co2_emmission
Kuwait Pork 0.00 0.00
United Arab Emirates Pork 0.00 0.00
Algeria Pork 0.00 0.00
Pakistan Pork 0.00 0.00
Saudi Arabia Pork 0.00 0.00
Tunisia Pork 0.00 0.00
Iran Pork 0.00 0.00
Bangladesh Pork 0.00 0.00
Oman Pork 0.01 0.04
Turkey Pork 0.01 0.04 
food_consumption %>%
  filter(food_category == "Milk - inc. cheese") %>%
  arrange(desc(consumption)) %>%
  head(10) %>% kable()
country food_category consumption co2_emmission
Finland Milk - inc. cheese 430.76 613.57
Netherlands Milk - inc. cheese 341.47 486.39
Sweden Milk - inc. cheese 341.23 486.05
Switzerland Milk - inc. cheese 318.69 453.94
Albania Milk - inc. cheese 303.72 432.62
Lithuania Milk - inc. cheese 295.46 420.85
Ireland Milk - inc. cheese 291.86 415.73
Kazakhstan Milk - inc. cheese 288.12 410.40
Estonia Milk - inc. cheese 284.85 405.74
Denmark Milk - inc. cheese 277.30 394.99 
food_consumption %>%
  filter(food_category == "Milk - inc. cheese") %>%
  arrange(consumption) %>%
  head(10) %>% kable()
country food_category consumption co2_emmission
Liberia Milk - inc. cheese 3.04 4.33
Cambodia Milk - inc. cheese 3.47 4.94
Mozambique Milk - inc. cheese 4.79 6.82
Sierra Leone Milk - inc. cheese 7.00 9.97
Rwanda Milk - inc. cheese 7.23 10.30
Nigeria Milk - inc. cheese 7.91 11.27
Togo Milk - inc. cheese 7.96 11.34
Malawi Milk - inc. cheese 7.98 11.37
Ghana Milk - inc. cheese 9.08 12.93
Zambia Milk - inc. cheese 9.71 13.83

Map

library(maps)

world <- map_data("world")

ggplot() + 
      geom_polygon(data = world, 
                   aes(x = long, y = lat, group = group), fill = NA, color = "black") 

food_consumption %>%
  filter(food_category == "Pork") %>%
  inner_join(world, by = c("country" = "region")) -> food_consumption_map

ggplot() + 
      geom_polygon(data = world, 
                   aes(x = long, y = lat, group = group), fill = NA, color = "black") + 
      geom_polygon(data = food_consumption_map, 
                 aes(x = long, y = lat, group = group, fill = consumption))

library(scales)

ggplot() + 
      geom_polygon(data = world, 
                   aes(x = long, y = lat, group = group), fill = NA, color = "black") + 
      geom_polygon(data = food_consumption_map, 
                 aes(x = long, y = lat, group = group, fill = consumption)) +
  scale_fill_distiller(palette = "Spectral", labels = number_format(suffix = " kg/person/year"))

ggplot() + 
      geom_polygon(data = world, 
                   aes(x = long, y = lat, group = group), fill = NA, color = "black") + 
      geom_polygon(data = food_consumption_map, 
                 aes(x = long, y = lat, group = group, fill = consumption)) +
  scale_fill_distiller(palette = "Spectral", labels = number_format(suffix = " kg/person/year")) +
  coord_fixed(1.3)

ggplot() + 
      geom_polygon(data = world, 
                   aes(x = long, y = lat, group = group), fill = NA, color = "black") + 
      geom_polygon(data = food_consumption_map, 
                 aes(x = long, y = lat, group = group, fill = consumption)) +
  scale_fill_distiller(palette = "Spectral", labels = number_format(suffix = " kg/person/year")) +
  coord_fixed(1.3) +
  theme_void() +
    theme(plot.margin = unit(c(0,0.1,0,0), "cm")) +
  labs(fill = "Pork Consumption")

# unique(food_consumption$food_category)

food_type <- "Milk - inc. cheese"

food_consumption %>%
  filter(food_category == food_type) %>%
  inner_join(world, by = c("country" = "region")) -> food_consumption_map


ggplot() + 
      geom_polygon(data = world, 
                   aes(x = long, y = lat, group = group), fill = NA, color = "black") + 
      geom_polygon(data = food_consumption_map, 
                 aes(x = long, y = lat, group = group, fill = consumption)) +
  scale_fill_distiller(palette = "Spectral", labels = number_format(suffix = " kg/person/year")) +
  coord_fixed(1.3) +
  theme_void() +
    theme(plot.margin = unit(c(0,0.1,0,0), "cm")) +
  labs(fill = paste0(food_type, " Consumption"))

Total Footprint

food_consumption %>%
  group_by(country) %>%
  summarise(total = sum(co2_emmission)) -> total_emissions

total_emissions %>%
  arrange(desc(total)) %>%
  head(10) %>% kable
country total
Argentina 2172.40
Australia 1938.66
Albania 1777.85
New Zealand 1750.95
Iceland 1731.36
USA 1718.86
Uruguay 1634.91
Brazil 1616.73
Luxembourg 1598.41
Kazakhstan 1575.08 
total_emissions %>%
  inner_join(world, by = c("country" = "region")) -> total_consumption_map

ggplot() + 
      geom_polygon(data = world, 
                   aes(x = long, y = lat, group = group), fill = NA, color = "black") + 
      geom_polygon(data = total_consumption_map, 
                 aes(x = long, y = lat, group = group, fill = total)) +
  scale_fill_distiller(palette = "Spectral", labels = comma_format(suffix = " CO2 kg/person/year")) +
  coord_fixed(1.3) +
  theme_void() +
    theme(plot.margin = unit(c(0,0.1,0,0), "cm")) +
  labs(fill = "Total Emissions")